Deep Learning: Feedforward Networks - Part 4

Deep Learning - Feedforward Networks - Part 4 This video explains backpropagation at the level of layer abstraction. Full Transcript https://towardsdatascience.com/lecture-notes-in-deep-learning-feedforward-networks-part-4-65593eb14aed Video References: Lex Fridman's Channel https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA References [1] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, inc., 2000. [2] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006. [3] F. Rosenblatt. “The perceptron: A probabilistic model for information storage and organization in the brain.” In: Psychological Review 65.6 (1958), pp. 386–408. [4] WS. McCulloch and W. Pitts. “A logical calculus of the ideas immanent in nervous activity.” In: Bulletin of mathematical biophysics 5 (1943), pp. 99–115. [5] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. “Learning representations by back-propagating errors.” In: Nature 323 (1986), pp. 533–536. [6] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. “Deep Sparse Rectifier Neural Networks”. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence Vol. 15. 2011, pp. 315–323. ​​​​​​​[7] William H. Press, Saul A. Teukolsky, William T. Vetterling, et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing. 3rd ed. New York, NY, USA: Cambridge University Press, 2007. Further Reading: A gentle Introduction to Deep Learning https://www.sciencedirect.com/science/article/pii/S093938891830120X Further Free Deep Learning Ressources (including exercises) https://lme.tf.fau.de/teaching/free-deep-learning-resources/